scholarly journals LONG-TERM FORECASTING OF EXTRAORDINARY SPRING FLOODS BY СOMMENSURABILITY METHOD ON THE DNIPRO RIVER NEAR THE KYIV CITY, UKRAINE

2019 ◽  
Vol 75 (2) ◽  
pp. 74-81
Author(s):  
Borys Fedorovich Khrystiuk ◽  
Liudmyla Olexandrivna Gorbachova
Keyword(s):  
Lead Time ◽  
Quantitative Methods ◽  
Practical Significance ◽  
The Past ◽  
Forecasting Method ◽  
Forecast Lead Time ◽  
Information Method ◽  
Long Term Forecasting ◽  
Industrial Center

The Kyiv city is the capital of Ukraine, as well as its major administrative and industrial center. Kyiv is located in the middle reaches of the Dnipro River which is the largest river in Ukraine. In the past, the Kyiv city suffered from dangerous spring floods. Consequently, long-term forecasting of spring floods on the Dnipro River near Kyiv has an important scientific and practical significance. Existing quantitative methods for such forecasting are of limited forecast lead time and require many input hydrometeorological data. In the paper the information method Weng Wen-Bo applied, which is a qualitative forecasting method. The use such method allows to determine the periods and specific years in which the following extraordinary spring floods on the Dnipro River near Kyiv can occur.

scholarly journals Comparison of the long-term forecasting method of RSSI by machine learning

2020 ◽  
Vol 9 (11) ◽  
pp. 553-558
Author(s):  
Tatsuya Nagao ◽  
Takahiro Hayashi ◽  
Yoshiaki Amano
Keyword(s):  
Machine Learning ◽  
Forecasting Method ◽  
Long Term Forecasting

scholarly journals Long-term streamflow forecasting using SWAT through the integration of the random forests precipitation generator: case study of Danjiangkou Reservoir

2017 ◽  
Vol 49 (5) ◽  
pp. 1513-1527 ◽  
Author(s):  
Zhongmin Liang ◽  
Tiantian Tang ◽  
Binquan Li ◽  
Tian Liu ◽  
Jun Wang ◽  
...  
Keyword(s):  
Hybrid Model ◽  
Random Forests ◽  
Lead Time ◽  
Swat Model ◽  
Monthly Precipitation ◽  
Streamflow Forecasting ◽  
Danjiangkou Reservoir ◽  
Forecast Lead Time ◽  
Reservoir Basin

Abstract Long-term streamflow forecasting is of great significance to the optimal management of water resources. However, the forecast lead time of long-term streamflow forecasting is relatively long and the forecasted precipitation within the forecast lead time has inherent uncertainty, so long-term streamflow forecasting has major challenges. In this paper, a hybrid forecasting model is developed to improve accuracy of long-term streamflow forecasting by combining random forests (RF) and the Soil and Water Assessment Tool (SWAT). The RF model is used to forecast monthly precipitation which is further downscaled to a daily dataset according to the hydrological similarity principle for use in the SWAT model of the Danjiangkou Reservoir basin, China. Performance of this hybrid model is compared to that of seasonal autoregressive (SAR (P)) model. Results show the RF precipitation generator yields accurate predictions at the monthly scale and the hybrid model produces acceptable streamflow series in long-term forecasting cases. In addition, the comparison shows that in the Danjiangkou Reservoir basin, the hybrid model performs better than the SAR (P) model, with average Nash–Sutcliffe efficiency (NSE) values of 0.94 and 0.51, which is better when it is closer to 1. This study provides a method of improving accuracy of long-term streamflow forecasting.

scholarly journals A Medium and Long-Term Runoff Forecast Method Based on Massive Meteorological Data and Machine Learning Algorithms

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1308
Author(s):  
Yujie Li ◽  
Dong Wang ◽  
Jing Wei ◽  
Bo Li ◽  
Bin Xu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lead Time ◽  
Machine Learning Algorithms ◽  
Classification And Regression Tree ◽  
Gradient Boosting ◽  
Forecast Performance ◽  
Forecast Lead Time ◽  
Extreme Gradient Boosting ◽  
Runoff Forecast

Accurate and reliable predictors selection and model construction are the key to medium and long-term runoff forecast. In this study, 130 climate indexes are utilized as the primary forecast factors. Partial Mutual Information (PMI), Recursive Feature Elimination (RFE) and Classification and Regression Tree (CART) are respectively employed as the typical algorithms of Filter, Wrapper and Embedded based on Feature Selection (FS) to obtain three final forecast schemes. Random Forest (RF) and Extreme Gradient Boosting (XGB) are respectively constructed as the representative models of Bagging and Boosting based on Ensemble Learning (EL) to realize the forecast of the three types of forecast lead time which contains monthly, seasonal and annual runoff sequences of the Three Gorges Reservoir in the Yangtze River Basin. This study aims to summarize and compare the applicability and accuracy of different FS methods and EL models in medium and long-term runoff forecast. The results show the following: (1) RFE method shows the best forecast performance in all different models and different forecast lead time. (2) RF and XGB models are suitable for medium and long-term runoff forecast but XGB presents the better forecast skills both in calibration and validation. (3) With the increase of the runoff magnitudes, the accuracy and reliability of forecast are improved. However, it is still difficult to establish accurate and reliable forecasts only large-scale climate indexes used. We conclude that the theoretical framework based on Machine Learning could be useful to water managers who focus on medium and long-term runoff forecast.

scholarly journals Forecasting the Daily Maximal and Minimal Temperatures from Radiosonde Measurements Using Neural Networks

2021 ◽  
Vol 11 (22) ◽  
pp. 10852
Author(s):  
Gregor Skok ◽  
Doruntina Hoxha ◽  
Žiga Zaplotnik
Keyword(s):  
Neural Networks ◽  
Lead Time ◽  
Forecast Error ◽  
Lead Times ◽  
Profile Measurement ◽  
Profile Data ◽  
Forecast Lead Time ◽  
Medium Range ◽  
Range Forecasts

This study investigates the potential of direct prediction of daily extremes of temperature at 2 m from a vertical profile measurement using neural networks (NNs). The analysis is based on 3800 daily profiles measured in the period 2004–2019. Various setups of dense sequential NNs are trained to predict the daily extremes at different lead times ranging from 0 to 500 days into the future. The short- to medium-range forecasts rely mainly on the profile data from the lowest layer—mostly on the temperature in the lowest 1 km. For the long-range forecasts (e.g., 100 days), the NN relies on the data from the whole troposphere. The error increases with forecast lead time, but at the same time, it exhibits periodic behavior for long lead times. The NN forecast beats the persistence forecast but becomes worse than the climatological forecast on day two or three. The forecast slightly improves when the previous-day measurements of temperature extremes are added as a predictor. The best forecast is obtained when the climatological value is added as well, with the biggest improvement in the long-term range where the error is constrained to the climatological forecast error.

scholarly journals Improving the Analog Ensemble Wind Speed Forecasts for Rare Events

2019 ◽  
Vol 147 (7) ◽  
pp. 2677-2692 ◽  
Author(s):  
Stefano Alessandrini ◽  
Simone Sperati ◽  
Luca Delle Monache
Keyword(s):  
Wind Speed ◽  
Bias Correction ◽  
Energy Demand ◽  
Lead Time ◽  
Rare Events ◽  
Training Dataset ◽  
Negative Bias ◽  
Meteorological Model ◽  
The Past ◽  
Forecast Lead Time

Abstract An analog-based ensemble technique, the analog ensemble (AnEn), has been applied successfully to generate probabilistic predictions of meteorological variables, wind and solar power, energy demand, and the optimal bidding in the day-ahead energy market. The AnEn method uses a historical time series of past forecasts from a meteorological model or other prediction systems and observations of the quantity to be predicted. For each forecast lead time, the ensemble set of predictions is a set of observations from the past. These observations are those concurrent with the past forecasts at the same lead time, chosen across the past runs most similar to the current forecast. Recent applications have demonstrated that the AnEn introduces a conditional negative bias when predicting events in the right tail of the forecast distribution of wind speed, particularly when the training dataset is short. This underestimation increases when the predicted event occurs less frequently in the available historical data. A new bias correction for the AnEn using wind observations from more than 500 U.S. stations is tested to reduce the AnEn’s underestimation of rare events. It is shown that the conditional negative bias introduced by the AnEn in its standard application is significantly reduced by our novel approach. Also, the overall probabilistic AnEn performances improve when predicting wind speed higher than 10 m s−1 as demonstrated by lower values of the continuous ranked probability score. These improvements can be attributed to an increased reliability achieved by introducing the proposed bias correction algorithm.

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